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Handbook of Network Analysis [KONECT -- the Koblenz Network Collection]
Computer Science > Social and Information Networks [Submitted on 22 Feb 2014 (v1), last revised 31 May 2026 (this version, v5)] Title:Handbook of Network Analysis [KONECT -- the Koblenz Network Collection] View PDF HTML (experimental)Abstract:This is the handbook for the KONECT project, the \emph{Koblenz Network Collection}, a scientific project to collect, analyse, and provide network datasets for researchers in all related fields of research, by the Namur Center for Complex Systems (naXys)...
Handbook of Network Analysis [KONECT -- the Koblenz Network Collection]
Computer Science > Social and Information Networks [Submitted on 22 Feb 2014 (v1), last revised 31 May 2026 (this version, v5)] Title:Handbook of Network Analysis [KONECT -- the Koblenz Network Collection] View PDF HTML (experimental)Abstract:This is the handbook for the KONECT project, the \emph{Koblenz Network Collection}, a scientific project to collect, analyse, and provide network datasets for researchers in all related fields of research, by the Namur Center for Complex Systems (naXys)...
GEMINI: Generalized Ensnarlment Measure from Incomplete-linkage of Network-network Interactions
arXiv:2606.05153v1 Announce Type: new Abstract: Spatially embedded networks are central to many physical and biological systems, where geometry and connectivity jointly shape structure and function. Examples abound across the scales of biological organization, from network-like membrane-bound organelles in the cell to mesoscale tissue organization of multiple distinct flow networks in organs and beyond. In each of these cases, the complexity of the architectures has heretofore frustrated our...
Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks
Announce Type: new Abstract: Network dynamics - including spreading, influence maximisation, and epidemic modelling - remain largely confined to the transductive paradigm, where models are trained on a single network and cannot be reused on unseen graphs without retraining. We argue that inductive cross-network generalisation is a necessary prerequisite for Graph Foundation Models (GFMs) in this domain and propose four design properties towards this goal. As a proof of concept, ts-net...
Social networks outsmart cognitive biases: How herding in networks makes populations more rational
Social networks outsmart cognitive biases: How herding in networks makes populations more rational Stephanie Baum Scientific Editor Andrew Zinin Lead Editor In 2010, the New York City-based restaurant Serendipity 3 revealed its $69 hot dog, winning the Guinness World Record for the world's most expensive hot dog. Served on a toasted pretzel roll with truffle butter and covered in foie gras, the award-winning hot dog made the restaurant's $18 cheeseburger seem like a steal. That's the point,...
MeshGuard: MUD-Based Network Access Control for Large-Scale Thread-Powered IoT Networks
Announce Type: new Abstract: The IETF standard Manufacturer Usage Description (MUD) enables manufacturers to equip IoT devices with certified URLs that provide traffic profiles for those devices, helping administrators enforce network access control. However, MUD assumes devices operate on full IP stacks and therefore does not account for constrained IoT devices running Thread--the dominant low-power mesh networking standard--which lacks complete TCP/IP functionality. While prior work...
Chaos-Free Networks are Stable Recurrent Neural Networks
Announce Type: replace-cross Abstract: Gated Recurrent Neural Networks (RNNs) are widely used for nonlinear system identification due to their high accuracy, although they often exhibit complex, chaotic dynamics that are difficult to analyze. This paper investigates the system-theoretic properties of the Chaos-Free Network (CFN), an architecture originally proposed to eliminate the chaotic behavior found in standard gated RNNs. First, we formally prove that the CFN satisfies Input-to-State...
On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection
Announce Type: new Abstract: Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are computationally demanding, motivating interest in lightweight alternatives suited to edge and neuromorphic deployment. Spiking Neural Networks (SNNs) are therefore a natural candidate, but their design space, spanning the choice of neuron model and spike encoding scheme, remains poorly characterized for intrusion...
Single-cell gene regulatory network reconstruction and key regulator identification using a dual-channel fusion graph convolutional network
Background and objective: Gene regulatory networks are formed by complex regulatory relationships between transcription factors and their target genes. A systematic understanding of these regulatory relationships is crucial for deciphering the molecular mechanisms that underlie cell state transitions under physiological and pathological conditions. Single-cell expression data can reveal cell-type-specific transcriptional regulation, and computational methods have recently been developed to...
AISC deployment in dynamic UAV-assisted MEC network: a reinforcement learning method based on heterogeneous graph attention neural network
Announce Type: new Abstract: Unmanned aerial vehicles-assisted mobile edge computing (UMEC) can execute compute-intensive and latency-critical artificial intelligence (AI) services, which can be provided by multiple UAVs collaborating in the air to perform inference tasks. Completing an AI service requires multiple inferences, each of which is implemented by an AI service chain consisting of multiple virtual network functions (VNFs). The application of AISC relies on an efficient AISC...